A framework that uses hidden Markov regimes and agent clustering to calibrate dynamic and heterogeneous parameters in agent-based models, reducing in-sample error on synthetic and real estate cases.
In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , IEEE, pp 1605–1610
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Automatic Calibration of Dynamic and Heterogeneous Parameters in Agent-based Model
A framework that uses hidden Markov regimes and agent clustering to calibrate dynamic and heterogeneous parameters in agent-based models, reducing in-sample error on synthetic and real estate cases.